Tootfinder

#ndwgoecountdown Jupiter: Gasriese im Sonnensystem
Jupiter ist der größte Planet im Sonnensystem; seine Wolkenwirbel und -bänder geben uns Rätsel auf. Unter Eisschichten verbergen seine Monde Europa und Ganymed Ozeane, in denen sich Leben entwickelt haben könnte. Das MPS stellt Weltrauminstrumente vor, die an Bord der Raumsonde JUICE auf dem Weg zum Jupiter sind. Mit Wolkenwir…

“How to avoid that your post about AI helps the hype”
https://hidde.blog/ai-hype/
Largely agree. I’m careful about the words I use on when I talk / post about it. I avoid terms that anthropomorphize, imply agency, reasoning, etc. I ask people to clarify *which* technology when they say “AI” (…

Signs that your post about AI helps the hype
If we're not cautious, we may accidentally feed the AI hype by talking about it in specific ways.

Às vezes, tudo o que um homem precisa é de um pouco de café. Me animei o suficiente para estar agora trocando de roupa para ir Š academia, jš que ontem eu não fui.

Further detail about what's been happening expanding on my thread about context: #USpol

Quantifying Azure RBAC Wildcard Overreach
Christophe Parisel
https://arxiv.org/abs/2506.10755 https://arxiv.org/pdf/2506.10755…

Quantifying Azure RBAC Wildcard Overreach
Azure RBAC leverages wildcard permissions to simplify policy authoring, but this abstraction often obscures the actual set of allowed operations and undermines least-privilege guarantees. We introduce Belshazaar, a two-stage framework that targets both the effective permission set problem and the evaluation of wildcards permissions spread. First, we formalize Azure action syntax via a context free grammar and implement a compiler that expands any wildcard into its explicit action set. Second, w…

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Sanjeev Ramkumar Sudha, Joel Jose, Erlend M. Coates
https://arxiv.org/abs/2507.13053

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Robotic information gathering (RIG) techniques refer to methods where mobile robots are used to acquire data about the physical environment with a suite of sensors. Informative planning is an important part of RIG where the goal is to find sequences of actions or paths that maximize efficiency or the quality of information collected. Many existing solutions solve this problem by assuming that the environment is known in advance. However, real environments could be unknown or time-varying, and a…

I'm not saying any new shit:
"I have almost reached the regrettable conclusion that the Negro's great stumbling block in his stride toward freedom is not the White Citizen's Counciler or the Ku Klux Klanner, but the white moderate, who is more devoted to "order" than to justice; who prefers a negative peace which is the absence of tension to a positive peace which is the presence of justice; who constantly says: "I agree with you in the goal you seek, but I cannot agree with your methods of direct action"; who paternalistically believes he can set the timetable for another man's freedom; who lives by a mythical concept of time and who constantly advises the Negro to wait for a "more convenient season." Shallow understanding from people of good will is more frustrating than absolute misunderstanding from people of ill will. Lukewarm acceptance is much more bewildering than outright rejection."
- Letter from a Birmingham Jail, MLK

Reinforcement Learning with Action Chunking
Qiyang Li, Zhiyuan Zhou, Sergey Levine
https://arxiv.org/abs/2507.07969 https://arxiv.org/pdf/2507.07969 https://arxiv.org/html/2507.07969
arXiv:2507.07969v1 Announce Type: new
Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks. Our recipe is designed for the offline-to-online RL setting, where the goal is to leverage an offline prior dataset to maximize the sample-efficiency of online learning. Effective exploration and sample-efficient learning remain central challenges in this setting, as it is not obvious how the offline data should be utilized to acquire a good exploratory policy. Our key insight is that action chunking, a technique popularized in imitation learning where sequences of future actions are predicted rather than a single action at each timestep, can be applied to temporal difference (TD)-based RL methods to mitigate the exploration challenge. Q-chunking adopts action chunking by directly running RL in a 'chunked' action space, enabling the agent to (1) leverage temporally consistent behaviors from offline data for more effective online exploration and (2) use unbiased $n$-step backups for more stable and efficient TD learning. Our experimental results demonstrate that Q-chunking exhibits strong offline performance and online sample efficiency, outperforming prior best offline-to-online methods on a range of long-horizon, sparse-reward manipulation tasks.
toXiv_bot_toot

"Men acquire a particular quality by constantly acting in a particular way."
-Aristotle
#acting #coaching #inspiration

#ndwgoecountdown Führungen durch das Max-Planck-Institut für Sonnensystemforschung
Ein Rundgang zu Laboren und Reinräumen des MPS Göttingen bietet Einblicke in Forschungsthemen, Arbeitsweise und aktuelle Weltraumprojekte des Instituts. Die Führungen dauern 60 Minuten und starten im 20-Minuten-Takt.
Teils enge Räumlichkeiten, Teilnehmerzahl beschränkt, Teilnahme nur nach…